ArticleJAMIA open2026
Development and validation of an algorithm to identify severe sepsis onset from electronic medical records.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop and evaluate an automated algorithm to identify sepsis onset, referred to as time-zero (t Materials and Methods: We developed an algorithm to construct a comprehensive timeline of systemic inflammatory response syndrome (SIRS) criteria and organ dysfunction (OD) using structured data, and documentation of infection (DOI) using both structured data and unstructured clinical notes. Algorithm performance was assessed using 2030 manually abstracted adult sepsis cases from a multicenter health system in southeast Michigan. Results: On average, the algorithm DOI time was significantly earlier than abstractors (mean: -0.33 hour, 95% Cl, -0.55 to -0.11), resulting in a significantly earlier t Discussion: Automated approaches to analyzing EMR data offer a scalable framework for SEP-1 monitoring, research, and quality improvement. Conclusion: Incorporating unstructured clinical notes improves DOI detection.
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